agent/papers/items/2026-2606-28187-gbc-gradient-based-connections-for-optimizing-multi-agent-systems.md

Paper: GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems

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type: paper title: "GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems" authors: Xiaocheng Yang, Abdulrahman Alrabah, Dilek Hakkani-Tür, Gokhan Tur year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.28187 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-26 updated_at: 2026-06-26 status: queued relevance: high topics:

• multi-agent
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 13 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: multi-agent, rag, tool-use
• arXiv categories: cs.MA
• collection score: 13

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.28187